Parisa Lak

Star Ratings versus Sentiment Analysis -- A Comparison of Explicit and Implicit Measures of Opinions

A typical trade-off in decision making is between the cost of acquiring information and the decline in decision quality caused by insufficient information. Consumers regularly face this trade-off in purchase decisions. Online product/service reviews serve as sources of product/service related information. Meanwhile, modern technology has led to an abundance of such content, which makes it prohibitively costly (if possible at all) to exhaust all available information. Consumers need to decide what subset of available information to use. Star ratings are excellent cues for this decision as they provide a quick indication of the tone of a review. However there are cases where such ratings are not available or detailed enough. Sentiment analysis -text analytic techniques that automatically detect the polarity of text- can help in these situations with more refined analysis. In this study, we compare sentiment analysis results with star ratings in three different domains to explore the promise of this technique.

How to Effectively Train IBM Watson: Classroom Experience

Watson is a question answering system that uses natural language processing, information retrieval, knowledge interpretation, automated reasoning and machine learning techniques. It can analyze millions of documents and answer most of the questions accurately with varying level of confidence. However, training IBM Watson may be tedious and may not be efficient if certain set of guidelines are not followed. In this paper, we discuss an effective strategy to train IBM Watson question answering system. We experienced this strategy during the classroom teaching of IBM Watson at Ryerson University in Big Data Analytics certification program. We have observed that if documents are well segmented, contain relevant titles and have consistent formatting, then the recall of the answers can be as high as 95%.

The Impact of Basic Matrix Factorization Refinements on Recommendation Accuracy

Consumers are commonly overloaded with various choices when it comes to the selection of a product or service. Many e-tailers have adopted built-in recommenders to help consumers make more informed decisions. While Accuracy of the recommender agents has high impact on customer satisfaction, achieving high accuracy in these systems is challenging. Various models and techniques were proposed in the literature to improve accuracy of these systems. Matrix factorization (MF) has been widely used in previous studies mostly to overcome cold start problem. In this study, we show that fine-tuning the parameters used in the basic MF model plays a significant role in achieving higher prediction accuracy. Our evaluations are performed on a basic model with and without simple user and item biases on two datasets.

Practice does not always make perfect: need for selection curricula in modern surgical training

BackgroundIt is hypothesized that not all surgical trainees are able to reach technical competence despite ongoing practice. The objectives of the study were to assess a trainees’ ability to reach technical competence by assessing learning patterns of the acquisition of surgical skills. Furthermore, it aims to determine whether individuals’ learning patterns were consistent across a range of open and laparoscopic tasks of variable difficulty.MethodsSixty-five preclinical medical students participated in a training curriculum with standardized feedback over forty repetitions of the following laparoscopic and open technical tasks: peg transfer (PT), circle cutting (CC), intracorporeal knot tie (IKT), one-handed tie, and simulated laparotomy closure. Data mining techniques were used to analyze the prospectively collected data and stratify the students into four learning clusters. Performance was compared between groups, and learning curve characteristics unique to trainees who have difficulty reaching technical competence were quantified.ResultsTop performers (22–35%) and high performers (32–42%) reached proficiency in all tasks. Moderate performers (25–37%) reached proficiency for all open tasks but not all laparoscopic tasks. Low performers (8–15%) failed to reach proficiency in four of five tasks including all laparoscopic tasks (PT 7.8%; CC 9.4%; IKT 15.6%). Participants in lower performance clusters demonstrated sustained performance disadvantage across tasks, with widely variable learning curves and no evidence of progression towards a plateau phase.ConclusionsMost students reached proficiency across a range of surgical tasks, but low-performing trainees failed to reach competence in laparoscopic tasks. With increasing use of laparoscopy in surgical practice, screening potential candidates to identify the lowest performers may be beneficial.